opts <- options(knitr.kable.NA = "")

1 Data overview

All EDGE samples excluded
OTU tables rarefied (16S: 9,048 & ITS: 10,116 seqs/sample)
‘Taxa’ are OTUs

AllG (All Grasses)

Bacterial & Archaeal Dataset:

Bac.AllG
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 23156 taxa and 533 samples ]
## sample_data() Sample Data:       [ 533 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 23156 taxa by 6 taxonomic ranks ]


Fungal Dataset:

Fun.AllG
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 6749 taxa and 509 samples ]
## sample_data() Sample Data:       [ 509 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 6749 taxa by 7 taxonomic ranks ]


ANGE (Andropogon gerardii / Big bluestem)

Bacterial & Archaeal Dataset:

Bac.ANGE
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 14268 taxa and 106 samples ]
## sample_data() Sample Data:       [ 106 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 14268 taxa by 6 taxonomic ranks ]


Fungal Dataset:

Fun.ANGE
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2985 taxa and 98 samples ]
## sample_data() Sample Data:       [ 98 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 2985 taxa by 7 taxonomic ranks ]


BOER (B. eriopoda / Black grama)

Bacterial & Archaeal Dataset:

Bac.BOER
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 8374 taxa and 84 samples ]
## sample_data() Sample Data:       [ 84 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 8374 taxa by 6 taxonomic ranks ]


Fungal Dataset:

Fun.BOER
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1662 taxa and 78 samples ]
## sample_data() Sample Data:       [ 78 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 1662 taxa by 7 taxonomic ranks ]


BOGR (B. gracilis / Blue grama)

Bacterial & Archaeal Dataset:

Bac.BOGR
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 10108 taxa and 86 samples ]
## sample_data() Sample Data:       [ 86 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 10108 taxa by 6 taxonomic ranks ]


Fungal Dataset:

Fun.BOGR
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2668 taxa and 92 samples ]
## sample_data() Sample Data:       [ 92 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 2668 taxa by 7 taxonomic ranks ]


BUDA (Bouteloua dactyloides / Buffalograss)

Bacterial & Archaeal Dataset:

Bac.BUDA
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 11048 taxa and 83 samples ]
## sample_data() Sample Data:       [ 83 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 11048 taxa by 6 taxonomic ranks ]


Fungal Dataset:

Fun.BUDA
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2369 taxa and 80 samples ]
## sample_data() Sample Data:       [ 80 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 2369 taxa by 7 taxonomic ranks ]


SCSC (Schizachyrium scoparium / Little bluestem)

Bacterial & Archaeal Dataset:

Bac.SCSC
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 17568 taxa and 174 samples ]
## sample_data() Sample Data:       [ 174 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 17568 taxa by 6 taxonomic ranks ]


Fungal Dataset:

Fun.SCSC
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 4347 taxa and 161 samples ]
## sample_data() Sample Data:       [ 161 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 4347 taxa by 7 taxonomic ranks ]


2 NMDS By Grass Host


- ANGE (Andropogon gerardii / Big bluestem)
- SCSC (Schizachyrium scoparium / Little bluestem)
- BOER (B. eriopoda / Black grama)
- BOGR (B. gracilis / Blue grama)
- BUDA (Bouteloua dactyloides / Buffalograss)

NMDS plots of all samples, colored by grass host
NMDS plots of all samples, colored by grass host
knitr::kable((as.data.frame(subset(adonis.out.array_predGrass, 
                    comm==comm.names.by.grass[1])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.AllG.Grass 4 18.6 0.132 20.12 0.001
Bac.AllG.Residual 528 122.2 0.868
Bac.AllG.Total 532 140.8 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predGrass, 
                    comm==comm.names.by.grass[2])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.AllG.Grass 4 13.2 0.064 8.67 0.001
Fun.AllG.Residual 504 192.5 0.936
Fun.AllG.Total 508 205.7 1.000

3 Subset by Grass Host

3.1 Influence of discrete latitudinal bins

AllG (All Grasses)

NMDS plots of all samples, colored by latitudinal bin
NMDS plots of all samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[1])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.AllG.Bin 3 7.9 0.056 10.48 0.001
Bac.AllG.Residual 529 132.9 0.944
Bac.AllG.Total 532 140.8 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[2])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.AllG.Bin 3 11.3 0.055 9.79 0.001
Fun.AllG.Residual 505 194.4 0.945
Fun.AllG.Total 508 205.7 1.000

ANGE (Andropogon gerardii / Big bluestem)

NMDS plots of ANGE host-associated samples, colored by latitudinal bin
NMDS plots of ANGE host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[3])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.ANGE.Bin 3 4.4 0.157 6.33 0.001
Bac.ANGE.Residual 102 23.8 0.843
Bac.ANGE.Total 105 28.2 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[4])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.ANGE.Bin 3 6.4 0.174 6.59 0.001
Fun.ANGE.Residual 94 30.6 0.826
Fun.ANGE.Total 97 37.0 1.000

BOER (B. eriopoda / Black grama)

NMDS plots of BOER host-associated samples, colored by latitudinal bin
NMDS plots of BOER host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[5])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.BOER.Bin 3 6 0.354 14.58 0.001
Bac.BOER.Residual 80 11 0.646
Bac.BOER.Total 83 17 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[6])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.BOER.Bin 3 5.4 0.194 5.94 0.001
Fun.BOER.Residual 74 22.3 0.806
Fun.BOER.Total 77 27.7 1.000

BOGR (B. gracilis / Blue grama)

NMDS plots of BOGR host-associated samples, colored by latitudinal bin
NMDS plots of BOGR host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[7])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.BOGR.Bin 3 2.9 0.168 5.51 0.001
Bac.BOGR.Residual 82 14.3 0.832
Bac.BOGR.Total 85 17.2 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[8])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.BOGR.Bin 3 4.0 0.126 4.24 0.001
Fun.BOGR.Residual 88 27.7 0.874
Fun.BOGR.Total 91 31.8 1.000

BUDA (Bouteloua dactyloides / Buffalograss)

NMDS plots of BUDA host-associated samples, colored by latitudinal bin
NMDS plots of BUDA host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[9])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.BUDA.Bin 3 2.6 0.171 5.45 0.001
Bac.BUDA.Residual 79 12.4 0.829
Bac.BUDA.Total 82 15.0 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[10])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.BUDA.Bin 3 4.3 0.14 4.11 0.001
Fun.BUDA.Residual 76 26.5 0.86
Fun.BUDA.Total 79 30.8 1.00

SCSC (Schizachyrium scoparium / Little bluestem)

NMDS plots of SCSC host-associated samples, colored by latitudinal bin
NMDS plots of SCSC host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[11])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.SCSC.Bin 3 4.0 0.09 5.58 0.001
Bac.SCSC.Residual 170 40.8 0.91
Bac.SCSC.Total 173 44.8 1.00
knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[12])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.SCSC.Bin 3 5.3 0.081 4.62 0.001
Fun.SCSC.Residual 157 60.0 0.919
Fun.SCSC.Total 160 65.3 1.000


3.2 Influence of discrete longitudinal gradients

AllG (All Grasses)

NMDS plots of all samples, colored by longitudinal gradient
NMDS plots of all samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[1])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.AllG.Gradient 2 17.5 0.124 37.57 0.001
Bac.AllG.Residual 530 123.3 0.876
Bac.AllG.Total 532 140.8 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[2])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.AllG.Gradient 2 10.4 0.05 13.45 0.001
Fun.AllG.Residual 506 195.3 0.95
Fun.AllG.Total 508 205.7 1.00

ANGE (Andropogon gerardii / Big bluestem)

NMDS plots of ANGE host-associated samples, colored by longitudinal gradient
NMDS plots of ANGE host-associated samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[3])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.ANGE.Gradient 1 2.8 0.1 11.62 0.001
Bac.ANGE.Residual 104 25.4 0.9
Bac.ANGE.Total 105 28.2 1.0
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[4])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.ANGE.Gradient 1 1.8 0.05 5.04 0.001
Fun.ANGE.Residual 96 35.2 0.95
Fun.ANGE.Total 97 37.0 1.00

BOER (B. eriopoda / Black grama)

NMDS plots of BOER host-associated samples, colored by longitudinal gradient






[NA on stats here - only 1 factor level]












# knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
#                     comm==comm.names.by.grass[5])))[,1:5], "simple", 
#              digits =c (0,1,3,2,3), 
#              align = 'c')

# knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
#                     comm==comm.names.by.grass[6])))[,1:5], "simple", 
#              digits =c (0,1,3,2,3), 
#              align = 'c')

BOGR (B. gracilis / Blue grama)

NMDS plots of BOGR host-associated samples, colored by longitudinal gradient
NMDS plots of BOGR host-associated samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[7])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.BOGR.Gradient 2 2.6 0.152 7.45 0.001
Bac.BOGR.Residual 83 14.5 0.848
Bac.BOGR.Total 85 17.2 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[8])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.BOGR.Gradient 2 3.5 0.109 5.46 0.001
Fun.BOGR.Residual 89 28.3 0.891
Fun.BOGR.Total 91 31.8 1.000

BUDA (Bouteloua dactyloides / Buffalograss)

NMDS plots of BUDA host-associated samples, colored by longitudinal gradient
NMDS plots of BUDA host-associated samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[9])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.BUDA.Gradient 2 2.6 0.172 8.3 0.001
Bac.BUDA.Residual 80 12.4 0.828
Bac.BUDA.Total 82 15.0 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[10])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.BUDA.Gradient 2 3.0 0.097 4.14 0.001
Fun.BUDA.Residual 77 27.8 0.903
Fun.BUDA.Total 79 30.8 1.000

SCSC (Schizachyrium scoparium / Little bluestem)

NMDS plots of SCSC host-associated samples, colored by longitudinal gradient
NMDS plots of SCSC host-associated samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[11])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Bac.SCSC.Gradient 2 5.9 0.131 12.93 0.001
Bac.SCSC.Residual 171 38.9 0.869
Bac.SCSC.Total 173 44.8 1.000
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[12])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')
Df SumOfSqs R2 F Pr(>F)
Fun.SCSC.Gradient 2 3.4 0.052 4.37 0.001
Fun.SCSC.Residual 158 61.8 0.948
Fun.SCSC.Total 160 65.3 1.000